add app
Browse files- app.py +92 -0
- requirements.txt +3 -0
app.py
ADDED
@@ -0,0 +1,92 @@
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from typing import List, Tuple
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import gradio as gr
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import numpy as np
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import torch
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
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tokenizer = GPT2Tokenizer.from_pretrained("dendee-geco_test-on-zuco1.0_gpt2_tmptoken_TRT_bs32_lr1e6_linearLR")
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model = GPT2LMHeadModel.from_pretrained('dendee-geco_test-on-zuco1.0_gpt2_tmptoken_TRT_bs32_lr1e6_linearLR', return_dict=True)
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model.to(device)
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def calculate_surprisals(
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input_text: str, normalize_surprisals: bool = True
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) -> Tuple[float, List[Tuple[str, float]]]:
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input_tokens = [
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token.replace("Ġ", "")
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for token in tokenizer.tokenize(input_text)
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if token != "▁"
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]
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input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)
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logits = model(input_ids)['logits'].squeeze(0)
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# can't calculate surprisals for the first token, hence 0
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surprisals = [0] + (- torch.gather(logits[:-1, :], -1, input_ids[:, 1:]).squeeze(0)).tolist()
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mean_surprisal = np.mean(surprisals[1:])
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if normalize_surprisals:
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min_surprisal = np.min(surprisals)
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max_surprisal = np.max(surprisals)
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surprisals = [
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(surprisal - min_surprisal) / (max_surprisal - min_surprisal)
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for surprisal in surprisals
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]
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assert min(surprisals) >= 0
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assert max(surprisals) <= 1
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tokens2surprisal: List[Tuple[str, float]] = []
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for token, surprisal in zip(input_tokens, surprisals):
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tokens2surprisal.append((token, surprisal))
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return mean_surprisal, tokens2surprisal
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def highlight_token(token: str, score: float):
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html_color = "#%02X%02X%02X" % (255, int(255 * (1 - score)), int(255 * (1 - score)))
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return '<span style="background-color: {}; color: black">{}</span>'.format(
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html_color, token
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)
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def create_highlighted_text(tokens2scores: List[Tuple[str, float]]):
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highlighted_text: str = ""
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for token, score in tokens2scores:
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highlighted_text += highlight_token(token, score)
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highlighted_text += "<br><br>"
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return highlighted_text
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def main(input_text: str) -> Tuple[float, str]:
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mean_surprisal, tokens2surprisal = calculate_surprisals(
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input_text, normalize_surprisals=True
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)
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highlighted_text = create_highlighted_text(tokens2surprisal)
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return round(mean_surprisal, 2), highlighted_text
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if __name__ == "__main__":
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demo = gr.Interface(
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fn=main,
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title="Demo",
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description="The input text is highlighted based on readability. (The higher the surprisal, the more difficult to read.)",
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inputs=gr.inputs.Textbox(
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lines=5,
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label="text",
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placeholder="input text here",
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),
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outputs=[
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gr.Number(label="surprisals"),
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gr.outputs.HTML(label="surprisals by token"),
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],
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examples=[
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"This is a sample text.",
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"Many girls insulted themselves.",
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"Many girls insulted herself.",
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"These casserols disgust Kayla.",
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"These casseroles disgusts Kayla."
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],
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)
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demo.launch()
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requirements.txt
ADDED
@@ -0,0 +1,3 @@
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torch==1.12.1
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transformers==4.20.0
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sentencepiece==0.1.97
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